• Advances in aircraft and spacecraft science
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• v.1 no.3
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• pp.291-310
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• 2014

An advanced model for the linear flutter analysis is introduced in this paper. Higher-order beam structural models are developed by using the Carrera Unified Formulation, which allows for the straightforward implementation of arbitrarily rich displacement fields without the need of a-priori kinematic assumptions. The strong form of the principle of virtual displacements is used to obtain the equations of motion and the natural boundary conditions for beams in free vibration. An exact dynamic stiffness matrix is then developed by relating the amplitudes of harmonically varying loads to those of the responses. The resulting dynamic stiffness matrix is used with particular reference to the Wittrick-Williams algorithm to carry out free vibration analyses. According to the doublet lattice method, the natural mode shapes are subsequently used as generalized motions for the generation of the unsteady aerodynamic generalized forces. Finally, the g-method is used to conduct flutter analyses of both isotropic and laminated composite lifting surfaces. The obtained results perfectly match those from 1D and 2D finite elements and those from experimental analyses. It can be stated that refined beam models are compulsory to deal with the flutter analysis of wing models whereas classical and lower-order models (up to the second-order) are not able to detect those flutter conditions that are characterized by bending-torsion couplings.

• Aerospace Engineering and Technology
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• v.6 no.1
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• pp.1-6
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• 2007

The present study provides the compressive design allowable of a unidirectional glass/epoxy composite laminate processed by wet lay-up at room temperature. The compressive strength values measured from 39 specimens have been assumed to follow the two-parameter Weibull distribution. Following the statistical guidelines provided by MIL-HDBK-17F, the B-basis and A-basis values of the aforementioned laminate are found to be 82.6% and 65.9%, of the mean compressive strength, respectively. The B-basis value is then discounted further at 50% for the in-situ application on the main wing spar caps of an experimental canard aircraft.

• Composites Research
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• v.32 no.1
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• pp.65-70
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• 2019

The aircraft composites wing parts are usually integrated with adhesive or fastener. These laminated composites have weak interlaminar strength, which can lead to delamination. In order to compensate the disadvantages of laminated composites, it is possible to improve the strength, durability, shock and fatigue resistance by reinforcing the fiber in the thickness direction. In addition, using a single structure near-net-shape saves the manufacturing time and the number of fasteners, thus can reduce the overall cost of the composite parts. In this study, compression test, tensile test and open-hole tensile test are carried out for three structural architecture of 3D (three-dimensional) textile preforms: orthogonal(ORT), layer-to-layer(LTL) and through-the-thickness(TTT) patterns. Among these, the orthogonal textile composite shows the highest Young's modulus and strength in tensile and compression. The notch sensitivity of the orthogonal textile composite was the smallest as compared with UD (unidirectional) and 2D (two-dimensional) fabric laminates.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.